code: def relaxed_caveman_graph(l, k, p, seed=None):
      """Returns a relaxed caveman graph.
  
      A relaxed caveman graph starts with `l` cliques of size `k`.  Edges are
      then randomly rewired with probability `p` to link different cliques.
  
      Parameters
      ----------
      l : int
        Number of groups
      k : int
        Size of cliques
      p : float
        Probability of rewiring each edge.
      seed : integer, random_state, or None (default)
          Indicator of random number generation state.
          See :ref:`Randomness<randomness>`.
  
      Returns
      -------
      G : NetworkX Graph
        Relaxed Caveman Graph
  
      Raises
      ------
      NetworkXError
       If p is not in [0,1]
  
      Examples
      --------
      >>> G = nx.relaxed_caveman_graph(2, 3, 0.1, seed=42)
  
      References
      ----------
      .. [1] Santo Fortunato, Community Detection in Graphs,
         Physics Reports Volume 486, Issues 3-5, February 2010, Pages 75-174.
         https://arxiv.org/abs/0906.0612
      """
      G = nx.caveman_graph(l, k)
      nodes = list(G)
      for u, v in G.edges():
          if seed.random() < p:  # rewire the edge
              x = seed.choice(nodes)
              if G.has_edge(u, x):
                  continue
              G.remove_edge(u, v)
              G.add_edge(u, x)
      return G
  
file_path: networkx/networkx/generators/community.py
imports: import itertools
  import math
  import networkx
  from networkx.utils import py_random_state
label_desc: Use image node_id 3 for calling a global function with example usage: relaxed_caveman_graph(l, k, p, seed) and returns: G